Machine learning allows software applications to predict outcomes without being programmed to yield a certain output. Basic algorithms receive input data, then predict and update the output as new data becomes available.
One of the requirements for machine learning is searching through data for patterns and then adjusting the system’s actions accordingly. Shopping on the Internet, for example, is a business that incorporates machine learning. Intelligent algorithms personalize ads to specific users so they are based on relevant browsing and shopping history. Other common machine learning applications include fraud detection, spam filtering, network security threat detection, predictive maintenance, and building news feeds.
Machine learning algorithms are often categorized as supervised or unsupervised. Supervised algorithms require input and desired output pairs to the system. Once training is complete, the algorithm will apply what was learned to new data.
Unsupervised algorithms, on the other hand, do not need to be trained at all. Instead, they use more complex processing on the unlabeled data. Their applications include image recognition, speech-to-text, and natural language generation.
Facebook's news feed is an excellent example of machine learning since it uses unsupervised algorithms to personalize each member's feed. These algorithms can also be used to analyze emails and prompt team members to respond to the most important messages first. Even human resource systems use learning models to identify characteristics of effective employees and then provide shortlists to narrow down the best applicants for open positions.
Another famous application of machine learning is self-driving cars, where neural networks are used to identify objects and determine optimal actions for safely steering a vehicle down the road. Smart assistants powered by machine learning combine several deep learning models to interpret natural speech and then bring in relevant context like a user's personal schedule or preferences.
Machine learning will continue to improve as more computational power is developed and more data becomes available for everyone.
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